code: def forward(self, batched_data):
      h_node = self.gnn_node(batched_data)
  
      h_graph = self.pool(h_node, batched_data.batch)
      output = self.graph_pred_linear(h_graph)
  
      if self.training:
          return output
      else:
          # At inference time, relu is applied to output to ensure positivity
          return torch.clamp(output, min=0, max=50)
  
file_path: ogb/examples/lsc/pcqm4m/gnn.py
imports: import torch
  from torch_geometric.nn import MessagePassing
  from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GlobalAttention, Set2Set
  import torch.nn.functional
  from torch_geometric.nn.inits import uniform
  from conv import GNN_node, GNN_node_Virtualnode
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.forward(batched_data) and returns: output, torch